A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913463062757376 |
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| author | Bonsu, Kwadwo Osei |
| author_facet | Bonsu, Kwadwo Osei |
| contents | This paper proposes a geometric approach for estimating the $α$ value in Q learning. We establish a systematic framework that optimizes the α parameter, thereby enhancing learning efficiency and stability. Our results show that there is a relationship between the learning rate and the angle between a vector T (total time steps in each episode of learning) and R (the reward vector for each episode). The concept of angular bisector between vectors T and R and Nash Equilibrium provide insight into estimating $α$ such that the algorithm minimizes losses arising from exploration-exploitation trade-off. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_04911 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm Bonsu, Kwadwo Osei Machine Learning Computer Science and Game Theory Theoretical Economics Optimization and Control This paper proposes a geometric approach for estimating the $α$ value in Q learning. We establish a systematic framework that optimizes the α parameter, thereby enhancing learning efficiency and stability. Our results show that there is a relationship between the learning rate and the angle between a vector T (total time steps in each episode of learning) and R (the reward vector for each episode). The concept of angular bisector between vectors T and R and Nash Equilibrium provide insight into estimating $α$ such that the algorithm minimizes losses arising from exploration-exploitation trade-off. |
| title | A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm |
| topic | Machine Learning Computer Science and Game Theory Theoretical Economics Optimization and Control |
| url | https://arxiv.org/abs/2408.04911 |